Trying to get started with `Flux.jl`

. I’m trying estimate a linear regression. I have the following:

```
using Flux
using Random
Random.seed!(1234)
β = [10., 20., 30.] # true parameters
predict(x) = (x*β)
function loss(x, y)
ŷ = predict(x)
sum((ŷ .- y).^2)
end
x = hcat(rand(500,2), ones(500,1)) # create data
y = x*β .+ 10*rand() # population equation
θ = Flux.Params([β])
gs = Flux.gradient( () -> loss(x, y), θ )
opt = Flux.Optimise.Descent()
Flux.train!(loss, θ, [(x, y)], opt)
β |> print
```

However I do not get back the OLS estimates `(x'*x)^(-1)*(x'*y)`

. Could there be an issue with the optimizer? I didn’t see any options for setting tolerance in the documentation.